Stability Across Activities

This code is for the results to the question: Is there stability across caregivers, regardless of activity?

Load libraries

library(tidyverse)
library(GGally)
library(ppcor)
library(psych)
library(Hmisc)
library(sjPlot)
library(ggpubr)

# https://github.com/ggobi/ggally/issues/139
my_custom_smooth <- function(data, mapping, ...) {
  ggplot(data = data, mapping = mapping) +
    geom_point(alpha = .4, color = I("black")) + 
    geom_smooth(method = "lm", color = I("blue"), ...)
}

theme_set(theme_bw())

Read in data and demographic information

# demographics
demo_english <- read_csv("./data_demo_lena_transcripts/demo_english_ms.csv") %>% 
  rename(id = ID, hi = HI24Final, momed = Momed) %>% 
  dplyr::select(id, hi, momed) %>% 
  mutate(id = as.character(id), 
         language = "english")

demo_spanish <- read_csv("./data_demo_lena_transcripts/demo_spanish_ms.csv") %>% 
  rename(id = ID, hi = HI_18, momed = MomEd_25m) %>% 
  dplyr::select(id, hi, momed) %>% 
  mutate(id = as.character(id), 
         language = "spanish")

demo <- rbind(demo_english, demo_spanish)


# NOTE about periods of non-tCDCS
# gemods refers to when there are designated start/end periods of other-directed speech (ODS); this was captured using gems (@G) using CHAT conventions
# kwalods refers to when ODS was transcribed at an utterance-level within a tCDS activity period between caregiver and child (e.g., other-directed speech in the background); this was captured per utterances using CHAT postcodes
## for tokens/min and types/min, we do not include ODS that occurred within a period of tCDS, because durations were captured by activity and not by utterance
## for mlu, we include all ODS across gemods and kwalods


# NOTE about speech == "all"
# "speech" includes two levels: all, spont
# all = refers to all speech by caregivers
# spont = refers to only speech by caregivers that was considered spontaneous rather than recited (e.g., reading book text, singing memorized common songs like itsy bitsy spider); therefore, 'spont' is a subset of 'all'


# freq
freq <- read_csv("./data_demo_lena_transcripts/freq.csv") %>% 
  filter(activity != "kwalods", 
         speech == "all") %>% 
  mutate(activity = recode(activity, "play" = "cc", "conv" = "cc", 
                           "food" = "cc", "routines" = "cc", "gemods" = "non_tcds")) %>% # we collapsed across play, routines, feeding, and unstructured conversation for the final paper [see supplemental for all categories]
  group_by(id, activity, segment_num, speaker) %>% 
  mutate(dur_min2 = sum(dur_min),
         tokens2 = sum(tokens), 
         types2 = mean(types)) %>% 
  distinct(id, activity, language, speaker, segment_num, tokens2, types2, dur_min2) %>% 
  mutate(tokens_permin2 = tokens2/dur_min2, 
         types_permin2 = types2/dur_min2) %>% 
  distinct(id, activity, language, speaker, segment_num, tokens_permin2, types_permin2) %>% 
  ungroup() %>% 
  mutate(activity = factor(activity, levels = c("books", "cc", "ac", "non_tcds")), 
         id = factor(id), 
         language = factor(language)) 




# mlu
mlu <- read_csv("./data_demo_lena_transcripts/mlu.csv") %>% 
  filter(speech == "all") %>% 
  mutate(activity = recode(activity, "play" = "cc", "conv" = "cc", 
                           "food" = "cc", "routines" = "cc", "ods" = "non_tcds")) %>% 
  group_by(id, activity, segment_num, speaker) %>% 
  mutate(words_sum2 = sum(words_sum),
         num_utt_sum2 = sum(num_utt_sum)) %>% 
  distinct(id, activity, language, speaker, segment_num, words_sum2, num_utt_sum2) %>% 
  group_by(id, activity, segment_num, speaker) %>% 
  mutate(mlu_w2 = words_sum2/num_utt_sum2) %>% 
  distinct(id, activity, language, speaker, segment_num, mlu_w2) %>% 
  ungroup() %>% 
  mutate(activity = factor(activity, levels = c("books", "cc", "ac", "non_tcds")), 
         id = factor(id), 
         language = factor(language))


# chip
# this includes only caregivers, therefore there is no speaker column
# we exclude periods of ODS because this is about responsiveness to the child during periods of tCDS
chip <- read_csv("./data_demo_lena_transcripts/chip.csv") %>% 
  filter(activity != "ods") %>% 
  mutate(activity = recode(activity, "play" = "cc", "conv" = "cc", 
                           "food" = "cc", "routines" = "cc")) %>% 
  group_by(id, activity, segment_num) %>% 
  mutate(total_adult_resp2 = sum(total_adult_resp),
         total_adult_imitexp2 = sum(total_adult_imitexp), 
         total_child_utt2 = sum(total_child_utt)) %>% 
  distinct(id, activity, language, segment_num, total_adult_resp2, total_adult_imitexp2, total_child_utt2) %>%
  mutate(prop_adultresp2 = total_adult_resp2/total_child_utt2, 
         prop_adult_imitexp2 = total_adult_imitexp2/total_child_utt2) %>% 
  distinct(id, activity, segment_num, language, prop_adultresp2, prop_adult_imitexp2) %>% 
  ungroup() %>% 
  mutate(activity = factor(activity, levels = c("books", "cc", "ac")), 
         id = factor(id), 
         language = factor(language))
  

str(freq)

## tibble [2,870 × 7] (S3: tbl_df/tbl/data.frame)
##  $ id            : Factor w/ 90 levels "7292","7352",..: 47 47 47 47 50 50 52 52 52 52 ...
##  $ activity      : Factor w/ 4 levels "books","cc","ac",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ language      : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
##  $ speaker       : chr [1:2870] "CHI" "ADULTS" "CHI" "ADULTS" ...
##  $ segment_num   : num [1:2870] 12 12 15 15 2 2 11 11 5 5 ...
##  $ tokens_permin2: num [1:2870] 8.46 42.57 5.32 21.75 12.31 ...
##  $ types_permin2 : num [1:2870] 4.79 19.73 2.59 9.89 3.61 ...

str(mlu)

## tibble [2,594 × 6] (S3: tbl_df/tbl/data.frame)
##  $ id         : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
##  $ activity   : Factor w/ 4 levels "books","cc","ac",..: 3 3 2 2 4 4 3 3 2 2 ...
##  $ language   : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
##  $ speaker    : chr [1:2594] "ADULTS" "CHI" "ADULTS" "CHI" ...
##  $ segment_num: num [1:2594] 2 2 2 2 2 2 3 3 3 3 ...
##  $ mlu_w2     : num [1:2594] 3.18 1.89 2.84 1.73 5.5 ...

str(chip)

## tibble [899 × 6] (S3: tbl_df/tbl/data.frame)
##  $ id                 : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
##  $ activity           : Factor w/ 3 levels "books","cc","ac": 3 2 3 2 3 2 2 3 2 3 ...
##  $ segment_num        : num [1:899] 2 2 3 3 4 4 5 7 7 8 ...
##  $ language           : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
##  $ prop_adultresp2    : num [1:899] 1.35 1.57 1.43 1.65 2.14 ...
##  $ prop_adult_imitexp2: num [1:899] 0.391 0.418 0.463 0.35 0.643 ...

Create dfs for ADULTS

# FREQ
freq_adult <- freq %>% 
  filter(speaker == "ADULTS")


# MLU
mlu_adult <- mlu %>% 
  filter(speaker == "ADULTS")

Create dfs averaging across segments for one obs per activity

Tokens and Types (rate per min)

# tokens and types: average per activity
freq_adult_per_activity_id <- freq_adult %>% 
  group_by(id, activity) %>% 
  mutate(tokens_permin_avg_act = mean(tokens_permin2), 
         types_permin_avg_act = mean(types_permin2)) %>% 
  distinct(id, activity, language, tokens_permin_avg_act, types_permin_avg_act) %>% 
  ungroup() %>% 
  mutate(activity = factor(activity, levels = c("books", "cc", "ac", "non_tcds")))

MLUw

# mlu: average per activity
mlu_adult_per_activity_id <- mlu_adult %>% 
  group_by(id, activity) %>% 
  mutate(mluw_avg_act = mean(mlu_w2)) %>% 
  distinct(id, activity, language, mluw_avg_act) %>% 
  ungroup() %>% 
  mutate(activity = factor(activity, levels = c("books", "cc", "ac", "non_tcds")))

Responses and Imitations/Expansions

# chip: average per activity
chip_per_activity_id <- chip %>% 
  group_by(id, activity) %>% 
  mutate(prop_adultresp_avg_act = mean(prop_adultresp2, na.rm = T), 
         prop_adult_imitexp_avg_act = mean(prop_adult_imitexp2, na.rm = T)) %>% 
  distinct(id, activity, language, prop_adultresp_avg_act, prop_adult_imitexp_avg_act)

Create wide dfs for matrices - tokens and types (rate)

# tokens
# all
tokens_mtx_rate <- freq_adult_per_activity_id %>% 
  dplyr::select(id, language, activity, tokens_permin_avg_act) %>% 
  pivot_wider(names_from = activity, values_from = tokens_permin_avg_act) %>% 
  ungroup() %>% 
  dplyr::select(c("language", "books", "cc", "ac", "non_tcds"))



# types
# all
types_mtx_rate <- freq_adult_per_activity_id %>% 
  dplyr::select(id, language, activity, types_permin_avg_act) %>% 
  pivot_wider(names_from = activity, values_from = types_permin_avg_act) %>% 
  ungroup() %>% 
  dplyr::select(c("language", "books", "cc", "ac", "non_tcds"))

Create wide dfs for matrices - mluw

# all
mlu_mtx <- mlu_adult_per_activity_id %>% 
  dplyr::select(id, language, activity, mluw_avg_act) %>% 
  pivot_wider(names_from = activity, values_from = mluw_avg_act) %>% 
  ungroup() %>% 
  dplyr::select(c("language", "books", "cc", "ac", "non_tcds"))

Create wide dfs for matrix - responses and imit/exp

# prop responses
# all
propresp_mtx <- chip_per_activity_id %>% 
  dplyr::select(id, language, activity, prop_adultresp_avg_act) %>% 
  pivot_wider(names_from = activity, values_from = prop_adultresp_avg_act) %>% 
  ungroup() %>% 
  dplyr::select(c("language", "books", "cc", "ac"))


# prop imitations and expansions
# all
propimitexp_mtx <- chip_per_activity_id %>% 
  dplyr::select(id, language, activity, prop_adult_imitexp_avg_act) %>% 
  pivot_wider(names_from = activity, values_from = prop_adult_imitexp_avg_act) %>% 
  ungroup() %>% 
  dplyr::select(c("language", "books", "cc", "ac"))

Zero-order correlations, one obs per ID (averaged across all activities)

# tokens and types rate
zero_corr_tokens_types <- freq_adult_per_activity_id %>% 
  ungroup() %>% 
  group_by(id) %>% 
  mutate(tokens_permin_avg_id = mean(tokens_permin_avg_act), 
         types_permin_avg_id = mean(types_permin_avg_act)) %>% 
  distinct(id, language, tokens_permin_avg_id, types_permin_avg_id)


# mlu
zero_corr_mlu <- mlu_adult_per_activity_id %>% 
  ungroup() %>% 
  group_by(id) %>% 
  mutate(mluw_mean_avg_id = mean(mluw_avg_act)) %>% 
  distinct(id, language, mluw_mean_avg_id)  


# chip
zero_corr_chip <- chip_per_activity_id %>% 
  ungroup() %>% 
  group_by(id) %>% 
  mutate(propresp_avg_id = mean(prop_adultresp_avg_act, na.rm = T), 
         propimitexp_avg_id = mean(prop_adult_imitexp_avg_act, na.rm = T)) %>% 
  distinct(id, language, propresp_avg_id, propimitexp_avg_id) 



# combining tokens, types, mlu
zero_corr_all_measures <- zero_corr_tokens_types %>% 
  full_join(zero_corr_mlu, by = c("id", "language")) %>% 
  full_join(zero_corr_chip, by = c("id", "language"))


# matrix
ggpairs(data = zero_corr_all_measures, 
        columns = 3:7, 
        lower = list(continuous = my_custom_smooth), 
        upper = list(continuous = wrap("cor", size = 9)),
        title = "zero order corr (avg'd across all activities) - tokens, types, mlu") + 
  theme(text= element_text(size = 18))

# correlation matrices between verbal engagement measures
# all
zero_corr_all_measures2 <- zero_corr_all_measures %>% 
  ungroup %>% 
  dplyr::select(-c(id, language))

rcorr(as.matrix(zero_corr_all_measures2), type = c("pearson"))

##                      tokens_permin_avg_id types_permin_avg_id mluw_mean_avg_id
## tokens_permin_avg_id                 1.00                0.89             0.62
## types_permin_avg_id                  0.89                1.00             0.42
## mluw_mean_avg_id                     0.62                0.42             1.00
## propresp_avg_id                      0.42                0.30             0.19
## propimitexp_avg_id                   0.24                0.16             0.20
##                      propresp_avg_id propimitexp_avg_id
## tokens_permin_avg_id            0.42               0.24
## types_permin_avg_id             0.30               0.16
## mluw_mean_avg_id                0.19               0.20
## propresp_avg_id                 1.00               0.42
## propimitexp_avg_id              0.42               1.00
## 
## n= 90 
## 
## 
## P
##                      tokens_permin_avg_id types_permin_avg_id mluw_mean_avg_id
## tokens_permin_avg_id                      0.0000              0.0000          
## types_permin_avg_id  0.0000                                   0.0000          
## mluw_mean_avg_id     0.0000               0.0000                              
## propresp_avg_id      0.0000               0.0036              0.0769          
## propimitexp_avg_id   0.0249               0.1218              0.0613          
##                      propresp_avg_id propimitexp_avg_id
## tokens_permin_avg_id 0.0000          0.0249            
## types_permin_avg_id  0.0036          0.1218            
## mluw_mean_avg_id     0.0769          0.0613            
## propresp_avg_id                      0.0000            
## propimitexp_avg_id   0.0000

Correlation Matrices - within each language

Tokens - Rate

ggpairs(data = tokens_mtx_rate, 
        columns = 2:5, 
        switch = 'y', 
        lower = list(continuous = my_custom_smooth),
        upper = list(continuous = wrap("cor", size = 7)),
        title = "Tokens rate (English and Spanish)") + 
  theme_classic() + 
  theme(text= element_text(size = 18),
        strip.placement = "outside",
        strip.text.y = element_text(face = "bold", size = 15), 
        strip.text.x = element_text(face = "bold", size = 15)) 

# correlation matrices
tokens_mtx_rate_clean <- tokens_mtx_rate %>% 
  dplyr::select(-language)
rcorr(as.matrix(tokens_mtx_rate_clean), type = c("pearson"))

##          books   cc   ac non_tcds
## books     1.00 0.65 0.29     0.34
## cc        0.65 1.00 0.26     0.51
## ac        0.29 0.26 1.00     0.24
## non_tcds  0.34 0.51 0.24     1.00
## 
## n
##          books cc ac non_tcds
## books       42 42 42       42
## cc          42 90 90       90
## ac          42 90 90       90
## non_tcds    42 90 90       90
## 
## P
##          books  cc     ac     non_tcds
## books           0.0000 0.0592 0.0276  
## cc       0.0000        0.0121 0.0000  
## ac       0.0592 0.0121        0.0238  
## non_tcds 0.0276 0.0000 0.0238

Types - Rate

ggpairs(data = types_mtx_rate, 
        columns = 2:5, 
        switch = 'y', 
        lower = list(continuous = my_custom_smooth),
        upper = list(continuous = wrap("cor", size = 7)),
        title = "Types rate (English and Spanish)") + 
  theme_classic() + 
  theme(text= element_text(size = 18),
        strip.placement = "outside",
        strip.text.y = element_text(face = "bold", size = 15), 
        strip.text.x = element_text(face = "bold", size = 15)) 

# correlation matrices
types_mtx_rate_clean <- types_mtx_rate %>% 
  dplyr::select(-language)
rcorr(as.matrix(types_mtx_rate_clean), type = c("pearson"))

##          books   cc   ac non_tcds
## books     1.00 0.23 0.13     0.00
## cc        0.23 1.00 0.08     0.22
## ac        0.13 0.08 1.00     0.19
## non_tcds  0.00 0.22 0.19     1.00
## 
## n
##          books cc ac non_tcds
## books       42 42 42       42
## cc          42 90 90       90
## ac          42 90 90       90
## non_tcds    42 90 90       90
## 
## P
##          books  cc     ac     non_tcds
## books           0.1377 0.3941 0.9904  
## cc       0.1377        0.4523 0.0354  
## ac       0.3941 0.4523        0.0738  
## non_tcds 0.9904 0.0354 0.0738

MLUw

mlu_mtx2 <- mlu_mtx %>% 
  rename("Books" = "books", "Other Child-cent" = "cc", "Adult-cent" = "ac", "non-tCDS" = "non_tcds")

ggpairs(data = mlu_mtx2, 
        columns = 2:5, 
        switch = 'y', 
        lower = list(continuous = my_custom_smooth),
        upper = list(continuous = wrap("cor", size = 11)),
        title = "MLUw (English and Spanish)") + 
  theme_classic() + 
  theme(text= element_text(size = 26),
        strip.placement = "outside",
        strip.text.y = element_text(face = "bold", size = 20), 
        strip.text.x = element_text(face = "bold", size = 20)) 

# correlation matrices
mlu_mtx_clean <- mlu_mtx %>% 
  dplyr::select(-language)
rcorr(as.matrix(mlu_mtx_clean), type = c("pearson"))

##          books   cc   ac non_tcds
## books     1.00 0.67 0.41     0.26
## cc        0.67 1.00 0.69     0.54
## ac        0.41 0.69 1.00     0.44
## non_tcds  0.26 0.54 0.44     1.00
## 
## n
##          books cc ac non_tcds
## books       42 42 42       42
## cc          42 90 90       90
## ac          42 90 90       90
## non_tcds    42 90 90       90
## 
## P
##          books  cc     ac     non_tcds
## books           0.0000 0.0066 0.0918  
## cc       0.0000        0.0000 0.0000  
## ac       0.0066 0.0000        0.0000  
## non_tcds 0.0918 0.0000 0.0000

PROP RESPONSES

ggpairs(data = propresp_mtx, 
        columns = 2:4, 
        switch = 'y', 
        lower = list(continuous = my_custom_smooth),
        upper = list(continuous = wrap("cor", size = 7)),
        title = "Prop Resp (English and Spanish)") + 
  theme_classic() + 
  theme(text= element_text(size = 18),
        strip.placement = "outside",
        strip.text.y = element_text(face = "bold", size = 15), 
        strip.text.x = element_text(face = "bold", size = 15)) 

# correlation matrices
propresp_mtx_clean <- propresp_mtx %>% 
  dplyr::select(-language)
rcorr(as.matrix(propresp_mtx_clean), type = c("pearson"))

##       books   cc   ac
## books  1.00 0.36 0.21
## cc     0.36 1.00 0.27
## ac     0.21 0.27 1.00
## 
## n
##       books cc ac
## books    42 42 39
## cc       42 89 86
## ac       39 86 87
## 
## P
##       books  cc     ac    
## books        0.0202 0.1959
## cc    0.0202        0.0114
## ac    0.1959 0.0114

PROP IMIT and EXP

ggpairs(data = propimitexp_mtx, 
        columns = 2:4, 
        switch = 'y', 
        lower = list(continuous = my_custom_smooth),
        upper = list(continuous = wrap("cor", size = 7)),
        title = "Prop Imit/Exp (English and Spanish)") + 
  theme_classic() + 
  theme(text= element_text(size = 18),
        strip.placement = "outside",
        strip.text.y = element_text(face = "bold", size = 15), 
        strip.text.x = element_text(face = "bold", size = 15)) 

# correlation matrices
propimitexp_mtx_clean <- propimitexp_mtx %>% 
  dplyr::select(-language)
rcorr(as.matrix(propimitexp_mtx_clean), type = c("pearson"))

##       books   cc   ac
## books  1.00 0.32 0.02
## cc     0.32 1.00 0.31
## ac     0.02 0.31 1.00
## 
## n
##       books cc ac
## books    42 42 39
## cc       42 89 86
## ac       39 86 87
## 
## P
##       books  cc     ac    
## books        0.0400 0.9082
## cc    0.0400        0.0043
## ac    0.9082 0.0043

Scatterplots of just Books and Other CC - INFANCY

# Tokens; r = .65, p < .001
tokens_books_cc <- ggplot(tokens_mtx_rate_clean, aes(books, cc)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  labs(x = "Books", y = "Other Child-Centered") +
  theme(text = element_text(size = 20)) + 
  annotate("text", x = 55, y = 125, size = 6, label = "Tokens per Min") + 
  annotate("text", x = 41, y = 115, size = 5, label = "r = .65")

# Types; r = .23, p = .14
types_books_cc <- ggplot(types_mtx_rate_clean, aes(books, cc)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  labs(x = "Books", y = "Other Child-Centered") +  
  theme(text = element_text(size = 20)) + 
  annotate("text", x = 19, y = 70, size = 6, label = "Types per Min") + 
  annotate("text", x = 14.5, y = 65, size = 5, label = "r = .23")

# MLUw; r = .67, p < .001
mlu_books_cc <- ggplot(mlu_mtx_clean, aes(books, cc)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  labs(x = "Books", y = "Other Child-Centered") +  
  theme(text = element_text(size = 20)) + 
  annotate("text", x = 2.2, y = 5, size = 6, label = "MLUw") + 
  annotate("text", x = 2.13, y = 4.7, size = 5, label = "r = .67")
  
  
# Prop Resp; r = .36, p = .020
resp_books_cc <- ggplot(propresp_mtx_clean, aes(books, cc)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  labs(x = "Books", y = "Other Child-Centered") +  
  theme(text = element_text(size = 20)) + 
  annotate("text", x = 2.2, y = 3.5, size = 6, label = "Proportion of Responses") + 
  annotate("text", x = 1.3, y = 3.3, size = 5, label = "r = .36")
  
  
# Imit/Exp; r = .32, p = .04
imitexp_books_cc <- ggplot(propimitexp_mtx_clean, aes(books, cc)) + 
  geom_point() + 
  geom_smooth(method = "lm") + 
  labs(x = "Books", y = "Other Child-Centered") +  
  theme(text = element_text(size = 20)) + 
  annotate("text", x = 0.37, y = 1, size = 6, label = "Proportion of Imit/Exp") + 
  annotate("text", x = 0.13, y = .93, size = 5, label = "r = .32")

ggarrange(tokens_books_cc, types_books_cc, mlu_books_cc, resp_books_cc, imitexp_books_cc)

ggsave("./figures/scatter_books_cc_only_aug1_2025.pdf", width = 14, height = 10, units = "in", dpi = 300)


# correlation
cor.test(tokens_mtx_rate_clean$books, tokens_mtx_rate_clean$cc)

## 
##  Pearson's product-moment correlation
## 
## data:  tokens_mtx_rate_clean$books and tokens_mtx_rate_clean$cc
## t = 5.3496, df = 40, p-value = 3.864e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4253680 0.7939095
## sample estimates:
##       cor 
## 0.6458076

cor.test(types_mtx_rate_clean$books, types_mtx_rate_clean$cc)

## 
##  Pearson's product-moment correlation
## 
## data:  types_mtx_rate_clean$books and types_mtx_rate_clean$cc
## t = 1.5147, df = 40, p-value = 0.1377
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.07643215  0.50135116
## sample estimates:
##       cor 
## 0.2329097

cor.test(mlu_mtx_clean$books, mlu_mtx_clean$cc)

## 
##  Pearson's product-moment correlation
## 
## data:  mlu_mtx_clean$books and mlu_mtx_clean$cc
## t = 5.7114, df = 40, p-value = 1.202e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4599859 0.8092956
## sample estimates:
##      cor 
## 0.670218

cor.test(propresp_mtx_clean$books, propresp_mtx_clean$cc)

## 
##  Pearson's product-moment correlation
## 
## data:  propresp_mtx_clean$books and propresp_mtx_clean$cc
## t = 2.4181, df = 40, p-value = 0.02025
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.05967333 0.59633176
## sample estimates:
##       cor 
## 0.3571276

cor.test(propimitexp_mtx_clean$books, propimitexp_mtx_clean$cc)

## 
##  Pearson's product-moment correlation
## 
## data:  propimitexp_mtx_clean$books and propimitexp_mtx_clean$cc
## t = 2.123, df = 40, p-value = 0.03999
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.01582542 0.56728987
## sample estimates:
##       cor 
## 0.3182262